Advancing global SMEFT fits in the LHC precision era

After more than a decade of data-taking, no direct evidence of new particles has been found at the LHC, suggesting that new physics (NP) might be heavier than the energies currently probed.

Heavy particles can alter interactions among known particles, causing subtle deviations from the Standard Model (SM) predictions.

The Standard Model Effective Field Theory (SMEFT) is a robust framework that allows for a model-independent parametrisation of new interactions, providing us with the tools needed for a successful indirect discovery program.

In particular, the peculiar structure of the theory dictates correlations between different observables, motivating global analyses. In this proposal, I develop a strategy to advance global SMEFT interpretations in three key directions.

Firstly, I will expand the comprehensiveness of the dataset by including low-energy observables in current global fit methodologies.

To accomplish this, renormalization group equations mixing effects must be included in theoretical predictions, as they prescribe how different energy regimes are connected.

Secondly, the research project aims to advance the design of optimal observables for global fits by using machine learning techniques, with the goal of maximising sensitivity to NP.

Lastly, the ultimate objective of indirect searches is the identification of heavy new particles responsible for the modified interactions; the SMEFT is simply an intermediate step in this endeavour.

I will provide the particle physics community with an open-source software that will interface with the output of global SMEFT fits and indicate which heavy particles are disfavoured by the data and which are still viable.The combination of my expertise in SMEFT analyses and collider phenomenology, along with the host institute's proficiency in advanced statistical methods, flavour physics, and UV matching, provides an ideal setting to successfully execute the proposed tasks and significantly advance indirect searches.